1. Technical Field
This disclosure relates to modeling retail store space. In particular, this disclosure relates to a top-down or aggregated approach for modeling and optimizing the physical layout parameters of a plurality of retail establishments using elasticity modeling.
2. Background
Sales and profitability of retail stores greatly depend upon the appeal and arrangement of the products on display. Most retail establishments display the products on shelving and display units, which may be arranged in aisles. Such display units and shelving may be arranged in various configurations having various dimensions. A planogram or POG is a map of fixtures and products that illustrate how the products are displayed, the number of products displayed, and the location or relative location where the products are displayed. Shelf space is a limited resource in the store environment, and carrying inventory also incurs a cost. Thus, retailers are interested in maximizing the returns from their available shelf space and inventory by making sure they create planograms that best allocate space to the various types of products.
However, it is not always clear just exactly how space related parameters affect store sales and profitability, and to what degree. For example, a retailer may need to change the amount of linear feet of display to add new products and/or eliminate existing products. Accordingly, management would prefer to eliminate products that have the least adverse impact on profitability. Management may also wish to identify products that contribute positively to incremental sales and profitability so as to maximize sales and profitability, and they may also wish to know in advance the likely result of remodeling a store or “re-flowing” the display arrangement or store aisle configuration.
In that regard, some statistical methods have been used to attempt to correlate individual products or “SKUs” with store metrics using regression analysis. However, known regression methods are inadequate when product turn-over is high and/or when new products are rapidly introduced. When a product lifecycle is fast and such products become obsolete quickly, it is difficult to analyze the corresponding data and make valid predictions regarding the likely effects of remodeling or product addition/deletion on store sales and profitability. For example, the turn-over of electronic goods in a retail store is extremely rapid, and typically within a two month period, 50% of the products may change completely. In addition, because of the large amounts of data required, some retailers may not wish to analyze their space allocation at an item or SKU level.
Because of the high turn-over and rapid obsolesce of certain retail products, or due to lack of data, there is a need for an analytical tool that can assist retail establishments in modeling, evaluating, and optimizing the impact of changing the product mix and display layout.